Workgreen2011

A robust F-measure for evaluating discovered process models

Jochen De Weerdt · Manu De Backer · Jan Vanthienen · Bart Baesens

119 citations0/0 files storedIEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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Business Process Modeling and Analysis Software Testing and Debugging Techniques Data Mining Algorithms and Applications

Abstract

Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a novel approach for evaluating discovered process models based on artificially generated negative events. This approach allows for the definition of a behavioral F-measure for discovered process models, which is the main contribution of this paper.

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References

2004Workflow mining: discovering process models from event logs 2007Conformance checking of processes based on monitoring real behavior 2007Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics 2007Genetic process mining: an experimental evaluation 2006Process mining with the HeuristicsMiner algorithm 2005Genetic Process Mining 2006Discovering expressive process models by clustering log traces 2007Mining process models with non-free-choice constructs

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2013Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining 2012Where Did I Misbehave? Diagnostic Information in Compliance Checking 2015Extracting Event Data from Databases to Unleash Process Mining 2014An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data 2017Complete and Interpretable Conformance Checking of Business Processes 2021

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